A Self-Adaptive Mapping Approach for Network on Chip With Low Power Consumption
Why this work is in the frame
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Bibliographic record
Abstract
Application mapping of disseminated intellectual property into Network on Chip (NoC) is a well-defined NP-Hard problem. Improvement of network performance in NoC is purely based on an effective mapping approach with cost and performance metrics optimization which includes area, power, delay, reliability, and thermal distribution. A self-adaptive mapping approach for NoC is proposed in this paper. In this method, the self-adaptive chicken swarm optimization algorithm (SCSO) is used for an effective mapping, which has never been applied with NoC. The proposed method reduces the power consumption of NoC through a cognitive base using shared K-nearest neighbor clustering method and it offers faster mapping over standard and randomly generated benchmarks. The experimental results indicate that the proposed method outperforms existing bio-inspired metaheuristic algorithms, especially for large application graph.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it